Search Results for author:
Found 7 papers, 3 papers with code
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation
D-Net is able to effectively utilize a multi-scale large receptive field and adaptively harness global contextual information.
Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ Segmentation
We evaluated Dynamic U-Net in two abdominal multi-organ segmentation benchmarks.
Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases.
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network
A 2. 5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status by leveraging imaging features from MR scans and prior knowledge features from clinical records and tumor location.
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)
Mean Dice scores were 0. 882 ($\pm$0. 244) and 0. 977 ($\pm$0. 04) for whole tumor segmentation for WUSM and MDA, respectively.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy
Deep neural networks with multilevel connections process input data in complex ways to learn the information. A networks learning efficiency depends not only on the complex neural network architecture but also on the input training images. Medical image segmentation with deep neural networks for skull stripping or tumor segmentation from magnetic resonance images enables learning both global and local features of the images. Though medical images are collected in a controlled environment, there may be artifacts or equipment based variance that cause inherent bias in the input set. In this study, we investigated the correlation between the image quality metrics of MR images with the neural network segmentation accuracy. For that we have used the 3D DenseNet architecture and let the network trained on the same input but applying different methodologies to select the training data set based on the IQM values. The difference in the segmentation accuracy between models based on the random training inputs with IQM based training inputs shed light on the role of image quality metrics on segmentation accuracy. By running the image quality metrics to choose the training inputs, further we may tune the learning efficiency of the network and the segmentation accuracy.
